class_docstr: The layer that trains fixed-length vector representations
  for the values of a discrete feature.
  It can work only with one feature. When several values of the feature
  are passed, the sum of the corresponding vectors is returned.
  
  :param input_layer: The input layer and the number of its output. If no number
      is specified, the first output will be connected.
  :type input_layer: object, tuple(object, int)
  :param count: The number of vectors in the representation table.
  :type count: int
  :param size: The length of each vector in the representation table.
  :type size: int
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  (1) a data blob with integer data that contains the feature values.
      The dimensions:
  
      - **BatchLength** * **BatchWidth** * **ListSize** equal to the number 
        of different values the feature can take
      - **Height** * **Width** * **Depth** * **Channels** equal to the number of values in the set
  
  .. rubric:: Layer outputs:
  
  (1) a blob with the sum of vector representations of the given feature values.
      The dimensions:
  
      - **BatchLength**, **BatchWidth**, **ListSize** equal to these dimensions of the input
      - **Height**, **Width**, **Depth** equal to 1
      - **Channels** equal to the vector length (size parameter below)
docstr: Sets the vector length.
          
docstr_tok: ["Sets","the","vector","length","."]
code: def size(self, size):
      """Sets the vector length."""
      self._internal.set_size(int(size))
  
file_path: neoml/NeoML/Python/neoml/Dnn/AccumulativeLookup.py
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
label_desc: Use image node_id 3 for calling the AccumulativeLookup obj's underlying member method code with example usage: obj.size(size) without return types